This project showcases how to fine-tune the powerful Mistral language model using the Unsloth library to build a robust and interactive math problem solver. It combines multiple components to enhance the user experience and ensure high accuracy:
- 🔧 Mistral Fine-Tuning with Unsloth for step-by-step mathematical reasoning.
- 🌐 Gradio Interface for easy, browser-based user interaction.
- 🧠 OpenAI API Integration to:
- 🖼️ Extract math problems from uploaded images (OCR + interpretation).
- ✅ Validate and correct Mistral's output if one or more of the generated answers are incorrect.
Large Language Models (LLMs) are increasingly being used in educational tools, especially for solving math problems. However, base models often struggle with step-by-step mathematical reasoning. In this project, we: 🧠 Technologies Used
- Mistral — Lightweight, high-performance LLM.
- Unsloth — Memory-optimized library for fast fine-tuning with LoRA.
- Gradio — Web-based UI for testing and deployment.
- OpenAI API — Used for image-to-text (problem extraction) and output validation.
You can see my training flow below
You can see my inference flow below
